7 papers
An LLM-powered Agentic Recommendation System for Connected TV Content Discovery
Lei Shi, Di Wang, Harry Tran +22
Recommendation systems, from traditional multi-stage to recent unified generative architectures, face challenges in incorporating diverse contextual signals, such as trending topic…
PathRouter: Aligning Rewards with Retrieval Quality in Agentic Graph Retrieval-Augmented Generation
Bo Wang, Heyan Huang, Yaolin Li +6
Agentic GraphRAG trains language-model agents to iteratively retrieve and reason over graph-structured evidence, enabling more accurate and context-aware decision-making by efficie…
LLM-Driven Reasoning for Constraint-Aware Feature Selection in Industrial Systems
Yuhang Zhou, Zhuokai Zhao, Ke Li +14
Feature selection is a crucial step in large-scale industrial machine learning systems, directly affecting model accuracy, efficiency, and maintainability. Traditional feature sele…
EBPO: Empirical Bayes Shrinkage for Stabilizing Group-Relative Policy Optimization
Kevin Han, Yuhang Zhou, Mingze Gao +6
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for enhancing the reasoning capabilities of Large Language Models (LLMs). However, dominant approaches li…
RecoWorld: Building Simulated Environments for Agentic Recommender Systems
Fei Liu, Xinyu Lin, Hanchao Yu +12
We present RecoWorld, a blueprint for building simulated environments tailored to agentic recommender systems. Such environments give agents a proper training space where they can…
Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding
Yuhang Zhou, Mingrui Zhang, Ke Li +12
Understanding and reasoning over tables is a critical capability for many real-world applications. Large language models (LLMs) have shown promise on this task, but current approac…